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Neural Comput. 2016 Oct;28(10):2063-90. doi: 10.1162/NECO_a_00876. Epub 2016 Aug 24.

Simulation Neurotechnologies for Advancing Brain Research: Parallelizing Large Networks in NEURON.

Author information

1
Departments of Physiology, Pharmacology, Biomedical Engineering, and Neurology, SUNY Downstate Medical Center, Brooklyn 11023, New York, and Kings County Hospital Center, Brooklyn 11203, New York, U.S.A. bill.lytton@downstate.edu.
2
Departments of Physiology, Pharmacology, Biomedical Engineering, and Neurology, SUNY Downstate Medical Center, Brooklyn, NY 11023, and Department of Chemical and Biomolecular Engineering, Tandon School of Engineering, New York University, Brooklyn, NY 11201, U.S.A. aseidenstein@icloud.com.
3
Departments of Physiology, Pharmacology, Biomedical Engineering, and Neurology, SUNY Downstate Medical Center, Brooklyn, NY 11023, U.S.A. salvadordura@gmail.com.
4
Department of Neuroscience, Yale University, New Haven, CT 06520, U.S.A. robert.mcdougal@yale.edu.
5
Blue Brain Project, Brain Mind Institute, Ecole Polytechnique Fédérale de Lausanne, 1015 Geneva, Switzerland felix.schuermann@epfl.ch.
6
Blue Brain Project, Brain Mind Institute, Ecole Polytechnique Fédérale de Lausanne, 1015 Geneva, Switzerland, and Department of Neuroscience, Yale University, New Haven, CT 06520, U.S.A. michael.hines@yale.edu.

Abstract

Large multiscale neuronal network simulations are of increasing value as more big data are gathered about brain wiring and organization under the auspices of a current major research initiative, such as Brain Research through Advancing Innovative Neurotechnologies. The development of these models requires new simulation technologies. We describe here the current use of the NEURON simulator with message passing interface (MPI) for simulation in the domain of moderately large networks on commonly available high-performance computers (HPCs). We discuss the basic layout of such simulations, including the methods of simulation setup, the run-time spike-passing paradigm, and postsimulation data storage and data management approaches. Using the Neuroscience Gateway, a portal for computational neuroscience that provides access to large HPCs, we benchmark simulations of neuronal networks of different sizes (500-100,000 cells), and using different numbers of nodes (1-256). We compare three types of networks, composed of either Izhikevich integrate-and-fire neurons (I&F), single-compartment Hodgkin-Huxley (HH) cells, or a hybrid network with half of each. Results show simulation run time increased approximately linearly with network size and decreased almost linearly with the number of nodes. Networks with I&F neurons were faster than HH networks, although differences were small since all tested cells were point neurons with a single compartment.

PMID:
27557104
PMCID:
PMC5295685
DOI:
10.1162/NECO_a_00876
[Indexed for MEDLINE]
Free PMC Article

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